A Novel Image Retrieval Based on Multi Resolution Color and Texture Features of Image Sub-blocks

In this paper we propose a new and efficient technique to retrieve images based on multi-resolution color and texture features of image sub-blocks. Firstly the image is divided into sub blocks of equal size in two resolutions. The size of the sub-block is fixed in two resolutions. Color of each sub-block is extracted by quantifying the HSV color space into non-equal intervals and the color feature is represented by cumulative histogram. Similarly the texture of the sub-block is extracted based on edge oriented gray tone spatial dependency matrix (GTSDM). An integrated matching scheme based on Most Similar minimum cost (MSMC) principle is used to compare the query and target image. The adjacency matrix of a bipartite graph is formed using the sub-blocks of query and target image. This matrix is used for matching the images. The experimental results show that the proposed method has achieved highest retrieval performance.